Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control

Abstract

This paper addresses synchronisation problem of high-order multi-input/multi-output (MIMO) multi-agent systems. Each agent has unknown nonlinear dynamics and is subject to uncertain external disturbances. The agents must follow a reference trajectory. An adaptive distributed controller based on relative information of neighbours of each agent is designed to solve the problem for any undirected connected communication topology. A radial basis function neural network is used to represent the controller's unknown structure. Lyapunov stability analysis is employed to guarantee stability of the overall system. By the theoretical analysis, the closed-loop control system is shown to be uniformly ultimately bounded. Finally, simulations are provided to show effectiveness of the proposed control method against uncertainty and disturbances.

Keywords:

• synchronisation
• adaptive control
• MIMO nonlinear systems
• neural network
• unknown dynamics

Additional information

Notes on contributors

Hassan Ghiti Sarand

Hassan Ghiti Sarand received his BS degree in bio medical engineering from Sahand University of Technology, Tabriz, Iran in 2003 and the MS degree in control engineering from Amirkabir University of Technology, Tehran, Iran, in 2006. He is currently pursuing the PhD degree in control engineering at Malek-Ashtar University of Technology, Isfahan, Iran. His research interests include robotics, intelligent systems, adaptive control, nonlinear control, multi-agent systems and control of drive systems.

Bahram Karimi

Bahram Karimi received his MSc degree in control engineering from Isfahan University of Technology, Isfahan, Iran, in 2000 and the PhD degree in control engineering from Amirkabir University of Technology, Tehran, Iran, in 2009. He is currently an assistant professor of electrical engineering department of Malek-Ashtar University of Technology, Isfahan, Iran. His research interests include large-scale systems, adaptive control, nonlinear control, and intelligent systems.